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torch.Size([1, 3, 448, 448])
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torch.Size([7, 3, 448, 448])
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Registering VQA_lavis step
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Registering EVAL step
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Registering RESULT step
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ANSWER0=VQA(image=LEFT,question='Is liquid being poured into a cup?')
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ANSWER1=EVAL(expr='{ANSWER0}')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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torch.Size([5, 3, 448, 448])
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question: ['Is there apparent damage to the bus in the image?'], responses:['yes']
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[WARNING|tokenization_utils_base.py:2697] 2024-10-22 17:17:22,941 >> Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation.
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question: ['Is the pair of shoes on the left of the single shoe?'], responses:['no']
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[WARNING|tokenization_utils_base.py:2697] 2024-10-22 17:17:22,959 >> Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation.
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question: ['Is liquid being poured into a cup?'], responses:['no']
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[WARNING|tokenization_utils_base.py:2697] 2024-10-22 17:17:23,282 >> Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation.
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question: ['How many animal species are in the image?'], responses:['1']
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[WARNING|tokenization_utils_base.py:2697] 2024-10-22 17:17:23,336 >> Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation.
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[('yes', 0.1298617250866936), ('congratulations', 0.12464161604141298), ('no', 0.12445222599225532), ('honey', 0.12437056445881921), ('solid', 0.12422595371654564), ('right', 0.12419889376311324), ('candle', 0.12414264780165109), ('chocolate', 0.12410637313950891)]
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[['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate']]
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[('no', 0.1313955057270409), ('yes', 0.12592208734904367), ('no smoking', 0.12472972590078177), ('gone', 0.12376514658020793), ('man', 0.12367833016285167), ('meow', 0.1235796378467502), ('kia', 0.12347643720898455), ('no clock', 0.12345312922433942)]
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[['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock']]
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torch.Size([1, 3, 448, 448]) knan debug pixel values shape
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torch.Size([3, 3, 448, 448]) knan debug pixel values shape
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dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 839
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dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 842
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dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 839
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[('no', 0.1313955057270409), ('yes', 0.12592208734904367), ('no smoking', 0.12472972590078177), ('gone', 0.12376514658020793), ('man', 0.12367833016285167), ('meow', 0.1235796378467502), ('kia', 0.12347643720898455), ('no clock', 0.12345312922433942)]
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[['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock']]
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dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 840
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[('1', 0.12829009354978346), ('3', 0.12529928082343206), ('4', 0.12464806219229535), ('8', 0.12460015878893425), ('6', 0.12451220062887247), ('12', 0.124338487048427), ('2', 0.12420459433498025), ('47', 0.12410712263327517)]
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[['1', '3', '4', '8', '6', '12', '2', '47']]
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tensor([4.9933e-01, 4.9933e-01, 2.5817e-05, 2.6338e-04, 3.0606e-04, 3.4874e-04,
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3.9058e-04, 1.0902e-05], device='cuda:2', grad_fn=<SoftmaxBackward0>)
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no *************
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['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([4.9933e-01, 4.9933e-01, 2.5817e-05, 2.6338e-04, 3.0606e-04, 3.4874e-04,
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3.9058e-04, 1.0902e-05], device='cuda:2', grad_fn=<SelectBackward0>)
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torch.Size([5, 3, 448, 448]) knan debug pixel values shape
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ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0.4993, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.4993, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0013, device='cuda:2', grad_fn=<DivBackward0>)}
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dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 839
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ANSWER0=VQA(image=RIGHT,question='How many shoes are in the image?')
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ANSWER1=EVAL(expr='{ANSWER0} == 1')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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torch.Size([7, 3, 448, 448])
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torch.Size([7, 3, 448, 448]) knan debug pixel values shape
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dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 839
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dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 840
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dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 840
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tensor([5.6711e-01, 1.3724e-02, 4.1491e-01, 1.9970e-03, 4.0551e-04, 9.5994e-04,
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9.0195e-05, 8.0902e-04], device='cuda:0', grad_fn=<SoftmaxBackward0>)
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yes *************
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['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([5.6711e-01, 1.3724e-02, 4.1491e-01, 1.9970e-03, 4.0551e-04, 9.5994e-04,
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9.0195e-05, 8.0902e-04], device='cuda:0', grad_fn=<SelectBackward0>)
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ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0.4149, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.5671, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0180, device='cuda:0', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=RIGHT,question='What color is the car?')
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ANSWER1=EVAL(expr='{ANSWER0} == "light blue"')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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torch.Size([13, 3, 448, 448])
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question: ['How many shoes are in the image?'], responses:['1']
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[('1', 0.12829009354978346), ('3', 0.12529928082343206), ('4', 0.12464806219229535), ('8', 0.12460015878893425), ('6', 0.12451220062887247), ('12', 0.124338487048427), ('2', 0.12420459433498025), ('47', 0.12410712263327517)]
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[['1', '3', '4', '8', '6', '12', '2', '47']]
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torch.Size([7, 3, 448, 448]) knan debug pixel values shape
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tensor([7.3011e-01, 2.6859e-01, 6.2645e-05, 2.6047e-04, 1.1166e-04, 1.5239e-04,
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5.7891e-04, 1.3085e-04], device='cuda:3', grad_fn=<SoftmaxBackward0>)
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no *************
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['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([7.3011e-01, 2.6859e-01, 6.2645e-05, 2.6047e-04, 1.1166e-04, 1.5239e-04,
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5.7891e-04, 1.3085e-04], device='cuda:3', grad_fn=<SelectBackward0>)
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ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0.2686, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.7301, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0013, device='cuda:3', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=LEFT,question='Is the case open?')
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ANSWER1=EVAL(expr='{ANSWER0}')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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torch.Size([7, 3, 448, 448])
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question: ['What color is the car?'], responses:['light']
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tensor([6.8600e-01, 3.0140e-02, 8.1122e-03, 1.9267e-03, 3.4890e-03, 1.4992e-03,
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2.6864e-01, 1.9551e-04], device='cuda:1', grad_fn=<SoftmaxBackward0>)
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1 *************
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['1', '3', '4', '8', '6', '12', '2', '47'] tensor([6.8600e-01, 3.0140e-02, 8.1122e-03, 1.9267e-03, 3.4890e-03, 1.4992e-03,
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2.6864e-01, 1.9551e-04], device='cuda:1', grad_fn=<SelectBackward0>)
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ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0.3140, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.6860, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:1', grad_fn=<DivBackward0>)}
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[('light', 0.1263865828213977), ('sunlight', 0.12497693187959452), ('lights', 0.1249334017905698), ('wine', 0.12483576877308507), ('water', 0.12478584053246268), ('glass', 0.12477465739247522), ('lamps', 0.12472148848257057), ('dark', 0.12458532832784439)]
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[['light', 'sunlight', 'lights', 'wine', 'water', 'glass', 'lamps', 'dark']]
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ANSWER0=VQA(image=LEFT,question='Is there a dish visible in the image?')
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ANSWER1=EVAL(expr='not {ANSWER0}')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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torch.Size([7, 3, 448, 448])
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question: ['Is the case open?'], responses:['yes']
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[('yes', 0.1298617250866936), ('congratulations', 0.12464161604141298), ('no', 0.12445222599225532), ('honey', 0.12437056445881921), ('solid', 0.12422595371654564), ('right', 0.12419889376311324), ('candle', 0.12414264780165109), ('chocolate', 0.12410637313950891)]
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[['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate']]
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torch.Size([13, 3, 448, 448]) knan debug pixel values shape
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3394
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torch.Size([7, 3, 448, 448]) knan debug pixel values shape
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3395
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question: ['Is there a dish visible in the image?'], responses:['no']
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tensor([9.4358e-01, 1.4325e-02, 7.4305e-03, 2.9042e-03, 4.2293e-03, 2.9479e-03,
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2.4371e-02, 2.1517e-04], device='cuda:2', grad_fn=<SoftmaxBackward0>)
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1 *************
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['1', '3', '4', '8', '6', '12', '2', '47'] tensor([9.4358e-01, 1.4325e-02, 7.4305e-03, 2.9042e-03, 4.2293e-03, 2.9479e-03,
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2.4371e-02, 2.1517e-04], device='cuda:2', grad_fn=<SelectBackward0>)
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ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0.9436, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.0564, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:2', grad_fn=<DivBackward0>)}
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[('no', 0.1313955057270409), ('yes', 0.12592208734904367), ('no smoking', 0.12472972590078177), ('gone', 0.12376514658020793), ('man', 0.12367833016285167), ('meow', 0.1235796378467502), ('kia', 0.12347643720898455), ('no clock', 0.12345312922433942)]
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[['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock']]
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